MetaMap: an atlas of metatranscriptomic reads in human disease-related RNA-seq data. Issue 6 (12th June 2018)

• Record Type: Journal Article
• Authors: Simon, L M; Karg, S; Westermann, A J; Engel, M; Elbehery, A H A; Hense, B; Heinig, M; Deng, L; Theis, F J
• Is Part Of: GigaScience. Volume 7:Issue 6(2018)
• Publication Date: 2018-06-12
• Subjects: high-performance computing -- big data -- RNA-seq -- sequence read archive -- metatranscriptomics -- microbiome -- virome -- human disease -- infection; Information storage and retrieval systems -- Research -- Periodicals; Biology -- Research -- Periodicals; Medical sciences -- Research -- Periodicals; Database management -- Periodicals; 570.285
• DOI: 10.1093/gigascience/giy070
• Languages: English
• ISSNs: 2047-217X

Abstract

Abstract: Background: With the advent of the age of big data in bioinformatics, large volumes of data and high-performance computing power enable researchers to perform re-analyses of publicly available datasets at an unprecedented scale. Ever more studies imply the microbiome in both normal human physiology and a wide range of diseases. RNA sequencing technology (RNA-seq) is commonly used to infer global eukaryotic gene expression patterns under defined conditions, including human disease-related contexts; however, its generic nature also enables the detection of microbial and viral transcripts. Findings: We developed a bioinformatic pipeline to screen existing human RNA-seq datasets for the presence of microbial and viral reads by re-inspecting the non-human-mapping read fraction. We validated this approach by recapitulating outcomes from six independent, controlled infection experiments of cell line models and compared them with an alternative metatranscriptomic mapping strategy. We then applied the pipeline to close to 150 terabytes of publicly available raw RNA-seq data from more than 17, 000 samples from more than 400 studies relevant to human disease using state-of-the-art high-performance computing systems. The resulting data from this large-scale re-analysis are made available in the presented MetaMap resource. Conclusions: Our results demonstrate that common human RNA-seq data, including those archived in public repositories, might contain valuable information to correlate microbial and viral detection patterns with diverse diseases. The presented MetaMap database thus provides a rich resource for hypothesis generation toward the role of the microbiome in human disease. Additionally, codes to process new datasets and perform statistical analyses are made available.